Image reconstruction is a related art technique that encompasses the entire image formation process and provides a foundation for the subsequent steps of image processing. Image reconstruction retrieves image information that has been lost during the process of image formation. In related art optical interference imaging techniques, interferograms of an object are converted into a wrapped phase map containing phase values in the range 0˜2π using a phase-shift algorithm. Three types of noise influence image reconstruction: residual noise, speckle noise, and noise at the lateral surface of height discontinuities. Speckle noise is usually generated due to the laser source. Residual noise is induced by environmental effects or contamination of the optical system.
The depth of field limit and the diffraction limit influence the optical measurement range, especially for microscope interferometers. For the object containing height discontinuities, the interferograms of the object will blur when the height of the object is out of the depth of field. In the depth of field, the position of height discontinuities creates difficulties in generating clear interferograms. Therefore, the blur of the interferograms at the height discontinuities will be converted into the noise in the wrapped phase map.
The wrapped phase maps are unwrapped by phase unwrapping algorithms. The phase unwrapping algorithms are classified as temporal, spatial and period-coding. Furthermore, the spatial phase unwrapping algorithms are classified as path-dependent algorithms (for example, MACY algorithm) and path-independent algorithms (for example, CA (cellular automata) algorithm). In the MACY algorithm, the phase unwrapping process is performed separately in row- and column-directions, respectively. The MACY is path-dependent and may suffer a disadvantage in that the unwrapping error caused by noise is accumulated progressively. Further, after its application, noise still exists at the lateral surface of height discontinuities. The CA algorithm accomplishes phase unwrapping using a multi-level iterative approach comprising both global and local cycles, known as global iteration and local iteration. Moreover, CA is path-independent, and thus the phase unwrapping errors caused by noise are not accumulated during the phase unwrapping procedure. However, its suffers from the disadvantage in that it is time consuming and the noise at the lateral surface of height discontinuities causes the CA algorithm to fail to seek the useful unwrapping paths.
When interferograms containing speckle noise are filtered by a linear filter, the whole images of interferograms are smeared to remove the speckle noise. Thus, the edges of the 2π phase jumps converted from interferograms are smoothed as shown in FIG. 2(b). Moreover, the noise at the lateral surface of height discontinuities is too turbulent, so the linear filter may not work effectively. Since the noise at the lateral surface is not removed clearly by the linear filter, the noise may cause phase unwrapping result of MACY and CA algorithms to fail.
These related art methods are not capable of removing all three noises: residual noise, speckle noise, and noise at the lateral surface of height discontinuities.
Thus, there is an unmet need for a method for detection and removal of noise in image reconstruction.